A systematic review of artificial intelligence in imaging - Pubrica
                     A SYSTEMATIC REVIEW 
OF  ARTIFICIAL 
INTELLIGENCE  IN 
IMAGING
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, 
Pubrica  Group: www.pubrica.com
Email: [email protected]
Today's Discussion
Outline In-Brief  
Introduction
Medical Imaging- A Systematic 
Review  FDA- Artificial Imaging
Evidence for Systematic Review
Additional benefits of Systematic 
Review  Future Scopes
Conclusion
In-Brief
Artificial intelligence offers a seizable promise for medical diagnostics.
Evaluation of the diagnostic accuracy of artificial intelligence algorithms process is 
 comparing it with the data of healthcare professional records. A systematic  
review of imaging techniques by artificial intelligence is useful here to research by  
biomedical researchers for their investigations. Pubrica is here to help you with
s ystematic review writing services to understand the various concepts of 
Artificial  intelligence in imaging techniques.
Introduction The significance of artificial intelligence has to change 
 daily life through its AI tools like speech recognition,  
robotics, etc.
Most of the healthcare sectors achieved great success 
 using artificial intelligence.
It is essential to c onduct a systematic study about 
the  artificial analytical tools for the various 
biomedical
researches.
Many reviews state that “the significance of artificial
intelligence will replace the medical disciplines or create 
 new job type for doctors and other clinicians”.
Medical  Diagnostic information for human using medical 
Imaging- A  imaging is one of the most valuable sources in 
 the  healthcare field.
Systemati
Data interpretation is facing more challenges.
c  Review
Despite hurdles, the diagnostic tool of medical
 imaging need is increasing as the available  
specialists cannot perform such complicated 
 tasks, especially in underdeveloped and  
developing countries.
Contd..
The diagnosis through AI using automated machines will understand deep 
 learning that will be able to solve the problem.
In recent years deep learning models exceed the human performance creates 
 excitement among the people.
However, there are many critical challenges raised against this new 
technology.
A s ystematic review writing about artificial intelligence and machine learning is  
essential to come up with a better conclusion about this new emerging technology.
FDA-
Art ificia Conducting a systematic review in the US Food and  
Drug Administration states that the systematic study of 
l   the body using an AI tool is harmless and gives rapid 
Imaging  results with 30 AI algorithms.
The government of the US implement the usage of 
 artificial intelligence in medical sectors.
All the medicos and clinicians are allowed to study the 
 instrumentation of synthetic intelligence.
Evidence  
for  They are deep learning through AI that promises in 
Systemati  improving the accuracy and speed of diagnosis for  
patients through medical imaging.
c  Review
Public interest in artificial intelligence is growing 
 every day and driving the market forces in  
diagnostic technologies.
Many studies developed or validated AI for the  
diagnostic feature of any diseases without any 
 restrictions in language.
Contd..
These studies recognise a change in model systems by creating deep learning 
 approaches, results in accurate algorithms using artificial intelligence when  
compared to humans.
No other systematic review is comparing performances of Artificial intelligence 
 and machine learning with the other medical professionals.
Many disease-specific s ystematic reviews are here using machine 
learning  technologies with reported algorithms.
Contd.
.

Addit iona
l  benefits The first s ystematic review is comparing the  
of diagnostic accuracy of all artificial intelligence  tools and machine learning models against  
Systemati professional clinicians using medical imaging  
c  Review published upto date.
Very few studies provide direct comparisons  
between deep learning and clinical professionals 
 validation histories.
The machine learning validation is more 
accurate.
Contd..
As per the meta analysis of deep learning techniques and health care  
professional analysis, clinicians can process many new algorithms, and external 
 proofs are also possible.
These sets a pathway to external validations in all predictive models.
Both healthcare and d eep learning algorithms overestimates internal 
guarantees.
Future 
The methodologies and process of studies are always 
  incomplete in deep learning techniques.
Scope
The level of diagnostic accuracy can be faster in future.
s
FDA will introduce New international standards of  
protocols in future and implement new learning methods.
The source of data interpretation will be better in future, 
and
writing a systematic literature review helps to understand the 
 concepts of deep learning techniques.
Conclusion
In this s ystematic review writing services under the  
guidance of Pubrica, the current state of diagnostic 
 performance using artificial intelligence in  
comparison with the healthcare professionals  
considering the daily issues faced by the world in  
medical sectors are studied.
A meta analysis of artificial intelligence and deep  
learning tools will help us to know more about the 
 future improvisions in medical fields.
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